Week Seven: Decision Science

Content:

This week Dr. Greenes covered the Basics of Decision Science where decision science is decision making using a statistical approach. Dr. Greenes in his lecture decision making in medicine is complex and that the goals, options, assessments, outcomes, etc are not always clear. The lecture then covered the basics of constructing a decision tree as well as solving one. Solving a decision tree involves the replacement of chance nodes with expected values and then working backwards from the terminal nodes to the starting decision node in a process that can be labeled backward induction. Sensitivity analysis of the decision tree was also considered to identify how the tree changes with question based changes, the robustness of the decision, as well as other analysis involving single or multiple parameters.


The second lecture focused mroe on the use and roles of decision analysis as well as its limitations. Decision Science then tries to be explicit/quantitative; however, it really depends on the input/data. Dr. Greenes sums this up nicely with GIGO (Garbage In, Garbage Out). Decision Trees were broken down into its elements: decision nodes, chance nodes, probabilities and utilities. It was covered that a decision tree node can have more than 2 branches, however it is important that the branches or choices are mutually exclusive. It was also important to recognize that chance nodes can come from two different types of data; objective (data from literature), and subjective (expert opinions). As well for the tree to be finished the terminal nodes have to be labeled with some value (ie Life Years - LY). In many cases such as those presented, the values are in LYs. Quality Adjusted Life Years can also be used for this purpose. The markov process was also covered. The Markov process is used when there is a continuous risk involving temporal sequence as well as multiple simultaneous events. This model assumes that the paient is always in a finite health state. The presentation then went on to cover conditional probability as well as sensitivity and specificity. It continued with the application of Baye's rule using some of the basic probability statements. Last but notable is that Dr. Greenes pointed out that decision science in the concepts presented this week are rarely used in actual case and are more prevelant in the development and determination of guidelines and policy.

Posted by Eric